Neighborhoods have been shown to be a critical foundation for shaping individuals' opportunities for achieving optimal health. As the number of cancer survivors continues to increase, with over 13 million in the U.S. currently, understanding factors that improve health-related quality of life (HRQOL) after cancer diagnosis is critical. Unless we identify and address the relevant contextual factors, interventions that only target individual factors are likely to have limited effectiveness. However, only three studies have considered neighborhood factors, despite that neighborhoods have been shown to influence health outcomes through a number of pathways--environmental exposures, material deprivation, psychosocial mechanisms, health behaviors, and access to resources. Therefore, we propose to leverage and pool individual-level data on sociodemographic, clinical, social, and behavioral factors from three existing cancer survivorship studies and merge them with neighborhood data. Specifically, we aim to (1) examine the associations between a comprehensive suite of social and built environment features and HRQOL, (a) adjusting for important covariates (e.g., clinical, demographic, social and behavioral factors) and (b) evaluating whether these associations vary by race/ethnicity, age, or gender, and (2) assess racial/ethnic disparities in HRQOL and evaluate whether these disparities are explained by clinical, demographic, social and behavioral factors, neighborhood factors, or a combination of them. To achieve these aims, with harmonized data across three California population-based studies (n=2,563, 46% minority)-Assessment of Patients' Experience of Cancer Care (APECC), Experiences of Care and Health Outcomes of survivors of Non-Hodgkin's Lymphoma (ECHOS-NHL), and Follow-up Care Use among Survivors (FOCUS)-we will geocode address at interview and merge the individual-level data with our social (e.g., socioeconomic, racial/ethnic composition, immigration) and built (e.g., population density, street connectivity, commuting, amenities, food environment) environment data. The main HRQOL outcomes will include physical and mental component summary scores. Linear regression models will be used to estimate unadjusted and adjusted associations between neighborhood features and the two HRQOL outcomes. Further, we will document racial/ethnic disparities in HRQOL and evaluate whether neighborhood features explain these disparities. This efficient study will be powered to detect significant neighborhoods effects across all of the cancer sites, as well as for 5 specific sites -breast, colorectal, gynecologic, NHL, and prostate. Our findings will provide the preliminary evidence for associations between the built and social environment and HRQOL and contribute significantly to our understanding of which neighborhood features influence HRQOL. Our collaboration with the Shanti Project will ensure that our study will meaningfully inform the design of multilevel interventions that not only account for individual factors that contribute to HRQOL, but also the context through which those individual factors are shaped.